High variability software systems can deliver their functionalities in multiple ways by reconfiguring their components. High variability has become important because of current trends towards software systems that come in product families, offer high levels of personalization, and fit well within a service-oriented architecture. The purpose of our research is to propose a framework that exploits such variability to allow a software system to self-repair in cases of failure. We propose an autonomic architecture that consists of monitoring, diagnosis, reconfiguration and execution components. This architecture uses requirements models as a basis for monitoring, diagnosis, and reconfiguration. We illustrate our proposal with a medium-sized publicly available case study (an automated teller machine (ATM) simulation), and evaluate its performance through a series of experiments. Our experimental results demonstrate that it is feasible to scale our approach to software systems with medium-size requirements.

Monitoring the satisfaction of software requirements and diagnosing what went wrong in case of failure is a hard problem that has received little attention in the Software and Requirement Engineering literature. To address this problem, we propose a framework adapted from artificial intelligence theories of action and diagnosis. Specifically, the framework monitors the satisfaction of software requirements and generates log data at a level of granularity that can be tuned adaptively at runtime depending on monitored feedback. When errors are found, the framework diagnoses the denial of the requirements and identifies problematic components. To support diagnostic reasoning, we transform the diagnostic problem into apropositional satisfiability (SAT) problem that can be solved by existing SAT solvers. We preprocess log data into a compact propositional encoding that better scales with problem size. The proposed theoretical framework has been implemented as a diagnosing component that will return sound and complete diagnoses accounting for observed aberrant system behaviors. Our solution is illustrated with two medium-sized publicly available case studies: a Web-based email client and an ATM simulation. Our experimental results demonstrate the feasibility of scaling our approach to medium-size software systems

We investigate the personalization capabilities of common personal software systems. We use a typical e-mail client as an example of such a system, and examine the configuration screens it offers to its users. We discover that each configuration value reflects each of the ways with which the user goals can be satisfied. Thus, we construct a goal model in which alternative ways for satisfying high level goals are matched with alternative system configurations. This way, automatic configuration of the system by reasoning about the overlaying goal model can be achieved. We find that the vast majority of the configuration options that refer to system functionality can be configured using this method, facilitating thereby the personalization tasks for users with no technical background, and ensuring, at the same time, consistency and meaningfulness in the configuration result.

A reverse engineering process aims at reconstructing high-level abstractions from source code. This paper presents a novel reverse engineering methodology for recovering stakeholder goal models from both structured and unstructured legacy code. The methodology consists of the following major steps: 1) Refactoring source code by extracting methods based on comments; 2) Converting the refactored code into an abstract structured program through statechart refactoring and hammock graph construction; 3) Extracting a goal model from the structured program's abstract syntax tree; 4) Identifying nonfunctional requirements and derive soft goals based on the traceability between the code and the goal model. To illustrate this requirements recovery process, we refactor stakeholder goal models from two legacy software code bases: an unstructured Web-based email in PHP (SquirrelMail) and a structured email client system in Java (Columba).

The web-services stack of standards is designed to support the reuse and interoperation of software components on the web. A critical step in the process of developing applications based on web services is service discovery, i.e. the identification of existing web services that can potentially be used in the context of a new web application. Discovery through catalog-style browsing (such as supported currently by web-service registries) is clearly insufficient. To support programmatic service discovery, we have developed a suite of methods that assess the similarity between two WSDL (Web Service Description Language) specifications based on the structure of their data types and operations and the semantics of their natural language descriptions and identifiers. Given only a textual description of the desired service, a semantic information-retrieval method can be used to identify and order the most relevant WSDL specifications based on the similarity of the element descriptions of the available specifications with the query. If a (potentially partial) specification of the desired service behavior is also available, this set of likely candidates can be further refined by a semantic structure-matching step, assessing the structural similarity of the desired vs the retrieved services and the semantic similarity of their identifiers. In this paper, we describe and experimentally evaluate our suite of service-similarity assessment methods.

The Web-services stack of standards is designed to support the reuse and interoperation of software components on the Web. A critical step, to that end, is service discovery, i.e., the identification of existing Web services that can potentially be used in the context of a new Web application. UDDI, the standard API for publishing Web-services specifications, provides a simple browsing-by-business-category mechanism for developers to review and select published services. In our work, we have developed a flexible service discovery method, for identifying potentially useful services and assessing their relevance to the task at hand. Given a textual description of the desired service, a traditional information-retrieval method is used to identify the most similar service description files, and to order them according to their similarity. Next, given this set of likely candidates and a (potentially partial) specification of the desired service behavior, a structure-matching step further refines and assesses the quality of the candidate service set. In this paper, we describe and experimentally evaluate our Web-service discovery process.